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Psychiatry faces a number of challenges due largely to the complexity of the relationship between mind and brain. Starting from the now well-justified assumption that the mind is instantiated in the physical substrate of the brain, understanding this relationship is going to be critical to any understanding of function and dysfunction. Key to that translation from physical substrate to mental function and dysfunction is the computational perspective: it provides a way of translating knowledge and understanding between levels of analysis (Churchland and Sejnowski 1994). Importantly, the computational perspective enables translation to both identify emergent properties (e.g., how a molecular change in a receptor affects behavior) and consequential properties (e.g., how an external sociological trauma can lead to circuit changes in neural processing). Given that psychiatry is about treating harmful dysfunction interacting across many levels (from subcellular to sociological), this chapter argues that the computational perspective is fundamental to understanding the relationship between mind and brain, and thus offers a new perspective on psychiatry.

In the opening chapters of this volume, we outlined a series of challenges facing psychiatry, as well as a description of its various promises, and suggested that taking a computational perspective could potentially illuminate a way forward. In this concluding chapter, we revisit these challenges and promises, in the context of what transpired at this Ernst Strüngmann Forum, to highlight the connections between the various themes raised. In particular, we will bring out the points of agreement and disagreement between the discussion groups and the chapters that arose from those discussions. We conclude with a description of the efforts, current and ongoing, to bring the potential synergy between psychiatry and computational neuroscience emphasized in this volume to a reality in the scientific and clinical arenas.

Biological psychiatry is at an impasse. Despite several decades of intense research, few, if any, biological parameters have contributed to a significant improvement in the life of a psychiatric patient. It is argued that this impasse may be a consequence of an obsessive focus on mechanisms. Alternatively, a risk prediction framework provides a more pragmatic approach, because it aims to develop tests and measures which generate clinically useful information. Computational approaches may have an important role to play here. This chapter presents an example of a risk-prediction framework, which shows that computational approaches provide a significant predictive advantage. Future directions and challenges are highlighted.